{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7267b7b0",
   "metadata": {},
   "source": [
    "# Using R packages for DID estimators in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ac0c55af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<rpy2.rinterface_lib.sexp.NULLType object at 0x00000197CE616FC0> [RTYPES.NILSXP]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from linearmodels.panel import PanelOLS\n",
    "\n",
    "import rpy2.robjects as ro\n",
    "from rpy2.robjects.packages import importr\n",
    "from rpy2.robjects import pandas2ri\n",
    "from rpy2.robjects.conversion import localconverter\n",
    "from rpy2.robjects import IntVector, Formula\n",
    "import rpy2.robjects.packages as rpackages\n",
    "utils = rpackages.importr('utils')\n",
    "utils.chooseCRANmirror(ind = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60b6a5a8",
   "metadata": {},
   "source": [
    "### Import R packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fa29a6c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "# Bacon Decomposition\n",
    "utils.install_packages(\"bacondecomp\")\n",
    "bacondecomp = rpackages.importr('bacondecomp')\n",
    "\n",
    "# Brantly Callaway, Pedro H.C. Sant’Anna (2020) estimator\n",
    "utils.install_packages(\"did\")\n",
    "did = rpackages.importr('did')\n",
    "\n",
    "# Clément de Chaisemartin, Xavier D’Haultfoeuille (2020)\n",
    "utils.install_packages(\"DIDmultiplegt\")\n",
    "DIDmultiplegt = rpackages.importr('DIDmultiplegt')\n",
    "\n",
    "# Pedro H.C. Sant’Anna , Jun Zhao (2020).\n",
    "utils.install_packages(\"DRDID\")\n",
    "DRDID = rpackages.importr('DRDID')\n",
    "\n",
    "# Liyang Sun, Sarah Abraham (2020)\n",
    "utils.install_packages(\"fixest\")\n",
    "fixest = rpackages.importr('fixest')\n",
    "\n",
    "# Kirill Borusyak, Xavier Jaravel, Jann Spiess (2021)\n",
    "utils.install_packages(\"didimputation\")\n",
    "didimputation = rpackages.importr('didimputation')\n",
    "\n",
    "# Gardner (2021)\n",
    "utils.install_packages(\"did2s\")\n",
    "did2s = rpackages.importr('did2s')\n",
    "\n",
    "# Broom package for working with fixest models\n",
    "utils.install_packages(\"broom\")\n",
    "broom = rpackages.importr('broom')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "689673de",
   "metadata": {},
   "source": [
    "### Example using Baker dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "5a8c49fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import data\n",
    "baker = pd.read_csv(\"baker.csv\")\n",
    "\n",
    "# Set groups that are never-treated to have a value of 0 \n",
    "# This is easier for conversion to an R/rpy2 DataFrame\n",
    "# Since we do not have any never-treated units for the Baker df, we can skip that step.\n",
    "\n",
    "# Convert the pandas df to an R/rpy2 DataFrame for use with the R packages\n",
    "with localconverter(ro.default_converter + pandas2ri.converter):\n",
    "      rbaker = ro.conversion.py2rpy(baker)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "551bea63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>firms</th>\n",
       "      <th>year</th>\n",
       "      <th>n</th>\n",
       "      <th>id</th>\n",
       "      <th>group</th>\n",
       "      <th>treat_date</th>\n",
       "      <th>treat</th>\n",
       "      <th>te</th>\n",
       "      <th>e</th>\n",
       "      <th>y2</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>individual</th>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1980</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>1980</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>10.030478</td>\n",
       "      <td>0.148687</td>\n",
       "      <td>1.149030</td>\n",
       "      <td>1.149030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>1981</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>9.976846</td>\n",
       "      <td>-0.238707</td>\n",
       "      <td>1.761637</td>\n",
       "      <td>1.761637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>1982</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>9.987340</td>\n",
       "      <td>0.188533</td>\n",
       "      <td>3.188877</td>\n",
       "      <td>3.188877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>1983</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>10.001581</td>\n",
       "      <td>0.181264</td>\n",
       "      <td>4.181607</td>\n",
       "      <td>4.181607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>1</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>1984</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1986</td>\n",
       "      <td>0</td>\n",
       "      <td>10.021217</td>\n",
       "      <td>-0.488355</td>\n",
       "      <td>4.511988</td>\n",
       "      <td>4.511988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1000</th>\n",
       "      <th>2005</th>\n",
       "      <td>40</td>\n",
       "      <td>4.954122</td>\n",
       "      <td>2005</td>\n",
       "      <td>26</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>4.014460</td>\n",
       "      <td>-0.037891</td>\n",
       "      <td>34.930690</td>\n",
       "      <td>38.945150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>40</td>\n",
       "      <td>4.954122</td>\n",
       "      <td>2006</td>\n",
       "      <td>27</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>4.017321</td>\n",
       "      <td>0.303873</td>\n",
       "      <td>36.275316</td>\n",
       "      <td>44.309958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>40</td>\n",
       "      <td>4.954122</td>\n",
       "      <td>2007</td>\n",
       "      <td>28</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>4.061008</td>\n",
       "      <td>0.372471</td>\n",
       "      <td>37.387601</td>\n",
       "      <td>49.570625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>40</td>\n",
       "      <td>4.954122</td>\n",
       "      <td>2008</td>\n",
       "      <td>29</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>3.978057</td>\n",
       "      <td>0.536419</td>\n",
       "      <td>38.468598</td>\n",
       "      <td>54.380825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>40</td>\n",
       "      <td>4.954122</td>\n",
       "      <td>2009</td>\n",
       "      <td>30</td>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>2004</td>\n",
       "      <td>1</td>\n",
       "      <td>3.916656</td>\n",
       "      <td>-0.000210</td>\n",
       "      <td>38.870568</td>\n",
       "      <td>58.453847</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30000 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 state     firms  year   n    id  group  treat_date  treat  \\\n",
       "individual time                                                              \n",
       "1          1980      1  0.000344  1980   1     1      1        1986      0   \n",
       "           1981      1  0.000344  1981   2     1      1        1986      0   \n",
       "           1982      1  0.000344  1982   3     1      1        1986      0   \n",
       "           1983      1  0.000344  1983   4     1      1        1986      0   \n",
       "           1984      1  0.000344  1984   5     1      1        1986      0   \n",
       "...                ...       ...   ...  ..   ...    ...         ...    ...   \n",
       "1000       2005     40  4.954122  2005  26  1000      4        2004      1   \n",
       "           2006     40  4.954122  2006  27  1000      4        2004      1   \n",
       "           2007     40  4.954122  2007  28  1000      4        2004      1   \n",
       "           2008     40  4.954122  2008  29  1000      4        2004      1   \n",
       "           2009     40  4.954122  2009  30  1000      4        2004      1   \n",
       "\n",
       "                        te         e         y2          y  \n",
       "individual time                                             \n",
       "1          1980  10.030478  0.148687   1.149030   1.149030  \n",
       "           1981   9.976846 -0.238707   1.761637   1.761637  \n",
       "           1982   9.987340  0.188533   3.188877   3.188877  \n",
       "           1983  10.001581  0.181264   4.181607   4.181607  \n",
       "           1984  10.021217 -0.488355   4.511988   4.511988  \n",
       "...                    ...       ...        ...        ...  \n",
       "1000       2005   4.014460 -0.037891  34.930690  38.945150  \n",
       "           2006   4.017321  0.303873  36.275316  44.309958  \n",
       "           2007   4.061008  0.372471  37.387601  49.570625  \n",
       "           2008   3.978057  0.536419  38.468598  54.380825  \n",
       "           2009   3.916656 -0.000210  38.870568  58.453847  \n",
       "\n",
       "[30000 rows x 12 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "baker = baker.set_index(['individual', 'time'])\n",
    "baker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ae57af15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the did2s R package to estimate TWFE, \n",
    "# Gardner (2021), \n",
    "# Callaway and Sant'Anna (2020),\n",
    "# Sun and Abraham (2020)\n",
    "# Borusyak, Jaravel, Spiess (2021)\n",
    "# Roth and Sant'Anna (2021)\n",
    "# This R package is helpful when you want to estimate a bunch of estimators at once and contrast them.\n",
    "# However, while we will focus on using individual commands for each estimators, in case this is helpful, \n",
    "# # The command has the following syntax, as defined for the Castle dataset.\n",
    "# Example code:\n",
    "# modelsr = did2s.event_study(data = rdata,\n",
    "#                  yname = 'l_homicide',\n",
    "#                  gname = 'effyear',\n",
    "#                  idname = 'sid',\n",
    "#                  tname = 'year')\n",
    "# Convert table containing estimates to a pandas df\n",
    "# with localconverter(ro.default_converter + pandas2ri.converter):\n",
    "#       models = ro.conversion.rpy2py(modelsr)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d515fb7c",
   "metadata": {},
   "source": [
    "## Code for estimating models in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5781dd2c",
   "metadata": {},
   "source": [
    "The following sub-sections gives sample code you can adapt to estimate models using the packages listed above."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f218d12",
   "metadata": {},
   "source": [
    "### TWFE "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "53e1f159",
   "metadata": {},
   "outputs": [],
   "source": [
    "baker['time_til'] = baker['year'] - baker['treat_date']\n",
    "baker['cons'] = 1\n",
    "baker = pd.concat([baker, pd.get_dummies(baker['time_til'], prefix = \"dd\")], axis = 1)\n",
    "dd = [dd for dd in baker.columns if dd.startswith(\"dd_\")]\n",
    "leads = {'dd_-1'}\n",
    "ind_cols = [ind for ind in dd if ind not in leads]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "ed726113",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sajidmu2\\Anaconda3\\lib\\site-packages\\linearmodels\\panel\\model.py:1831: AbsorbingEffectWarning: \n",
      "Variables have been fully absorbed and have removed from the regression:\n",
      "\n",
      "dd_18, dd_19, dd_20, dd_21, dd_22, dd_23\n",
      "\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "twfe = PanelOLS(baker.y, baker[['cons'] + ind_cols],  \n",
    "                         entity_effects = True, time_effects = True,\n",
    "                         check_rank = True, drop_absorbed = True).fit(cov_type = 'robust')\n",
    "\n",
    "het_err_series = twfe.params - twfe.conf_int()['lower']\n",
    "het_coef_df = pd.DataFrame({'coef': twfe.params.values[1:48],\n",
    "                        'err': het_err_series.values[1:48],\n",
    "                        'varname': het_err_series.index.values[1:48]\n",
    "                       })\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (16, 10))\n",
    "het_coef_df.plot(x = 'varname', y = 'coef', kind = 'bar', \n",
    "             ax = ax, color = 'none', \n",
    "             yerr = 'err', legend = False)\n",
    "ax.set_ylabel('')\n",
    "ax.set_xlabel('')\n",
    "ax.axhline(y = 0, color= 'r', linestyle='-')\n",
    "ax.scatter(x = np.arange(het_coef_df.shape[0]), \n",
    "           marker = 'o', s = 10, \n",
    "           y = het_coef_df['coef'], color = 'black')\n",
    "ax.xaxis.set_ticks_position('none')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4029b218",
   "metadata": {},
   "source": [
    "### Callaway and Sant'Anna (2020) - did package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4373c59a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>type</th>\n",
       "      <th>term</th>\n",
       "      <th>event.time</th>\n",
       "      <th>estimate</th>\n",
       "      <th>std.error</th>\n",
       "      <th>conf.low</th>\n",
       "      <th>conf.high</th>\n",
       "      <th>point.conf.low</th>\n",
       "      <th>point.conf.high</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-17)</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>-0.017531</td>\n",
       "      <td>0.027248</td>\n",
       "      <td>-0.101502</td>\n",
       "      <td>0.066439</td>\n",
       "      <td>-0.070937</td>\n",
       "      <td>0.035874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-16)</td>\n",
       "      <td>-16.0</td>\n",
       "      <td>-0.009897</td>\n",
       "      <td>0.026398</td>\n",
       "      <td>-0.091246</td>\n",
       "      <td>0.071452</td>\n",
       "      <td>-0.061635</td>\n",
       "      <td>0.041842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-15)</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>0.033861</td>\n",
       "      <td>0.025256</td>\n",
       "      <td>-0.043969</td>\n",
       "      <td>0.111691</td>\n",
       "      <td>-0.015639</td>\n",
       "      <td>0.083361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-14)</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-0.008687</td>\n",
       "      <td>0.026462</td>\n",
       "      <td>-0.090234</td>\n",
       "      <td>0.072860</td>\n",
       "      <td>-0.060551</td>\n",
       "      <td>0.043177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-13)</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>0.010769</td>\n",
       "      <td>0.025868</td>\n",
       "      <td>-0.068949</td>\n",
       "      <td>0.090486</td>\n",
       "      <td>-0.039932</td>\n",
       "      <td>0.061469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-12)</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-0.024517</td>\n",
       "      <td>0.024753</td>\n",
       "      <td>-0.100798</td>\n",
       "      <td>0.051764</td>\n",
       "      <td>-0.073032</td>\n",
       "      <td>0.023998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-11)</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>-0.002178</td>\n",
       "      <td>0.020089</td>\n",
       "      <td>-0.064086</td>\n",
       "      <td>0.059730</td>\n",
       "      <td>-0.041552</td>\n",
       "      <td>0.037196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-10)</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>0.024891</td>\n",
       "      <td>0.018604</td>\n",
       "      <td>-0.032442</td>\n",
       "      <td>0.082223</td>\n",
       "      <td>-0.011573</td>\n",
       "      <td>0.061354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-9)</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>-0.016160</td>\n",
       "      <td>0.018425</td>\n",
       "      <td>-0.072940</td>\n",
       "      <td>0.040619</td>\n",
       "      <td>-0.052272</td>\n",
       "      <td>0.019952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-8)</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>0.017224</td>\n",
       "      <td>0.019280</td>\n",
       "      <td>-0.042192</td>\n",
       "      <td>0.076639</td>\n",
       "      <td>-0.020565</td>\n",
       "      <td>0.055012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-7)</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>-0.000583</td>\n",
       "      <td>0.017486</td>\n",
       "      <td>-0.054470</td>\n",
       "      <td>0.053303</td>\n",
       "      <td>-0.034856</td>\n",
       "      <td>0.033689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-6)</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>-0.026200</td>\n",
       "      <td>0.021407</td>\n",
       "      <td>-0.092169</td>\n",
       "      <td>0.039770</td>\n",
       "      <td>-0.068156</td>\n",
       "      <td>0.015757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-5)</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>0.014351</td>\n",
       "      <td>0.016567</td>\n",
       "      <td>-0.036703</td>\n",
       "      <td>0.065405</td>\n",
       "      <td>-0.018120</td>\n",
       "      <td>0.046821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-4)</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-0.014417</td>\n",
       "      <td>0.016100</td>\n",
       "      <td>-0.064032</td>\n",
       "      <td>0.035199</td>\n",
       "      <td>-0.045973</td>\n",
       "      <td>0.017139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-3)</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-0.009011</td>\n",
       "      <td>0.016761</td>\n",
       "      <td>-0.060662</td>\n",
       "      <td>0.042640</td>\n",
       "      <td>-0.041861</td>\n",
       "      <td>0.023839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-2)</td>\n",
       "      <td>-2.0</td>\n",
       "      <td>-0.007238</td>\n",
       "      <td>0.016655</td>\n",
       "      <td>-0.058563</td>\n",
       "      <td>0.044086</td>\n",
       "      <td>-0.039881</td>\n",
       "      <td>0.025404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(-1)</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.009600</td>\n",
       "      <td>0.016298</td>\n",
       "      <td>-0.040625</td>\n",
       "      <td>0.059824</td>\n",
       "      <td>-0.022343</td>\n",
       "      <td>0.041542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(0)</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.002456</td>\n",
       "      <td>0.062152</td>\n",
       "      <td>7.810924</td>\n",
       "      <td>8.193988</td>\n",
       "      <td>7.880641</td>\n",
       "      <td>8.124271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(1)</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.010234</td>\n",
       "      <td>0.124710</td>\n",
       "      <td>15.625914</td>\n",
       "      <td>16.394553</td>\n",
       "      <td>15.765806</td>\n",
       "      <td>16.254662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(2)</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.020465</td>\n",
       "      <td>0.167827</td>\n",
       "      <td>23.503274</td>\n",
       "      <td>24.537655</td>\n",
       "      <td>23.691531</td>\n",
       "      <td>24.349399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(3)</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32.013758</td>\n",
       "      <td>0.225303</td>\n",
       "      <td>31.319443</td>\n",
       "      <td>32.708073</td>\n",
       "      <td>31.572172</td>\n",
       "      <td>32.455344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(4)</td>\n",
       "      <td>4.0</td>\n",
       "      <td>40.031719</td>\n",
       "      <td>0.298055</td>\n",
       "      <td>39.113206</td>\n",
       "      <td>40.950232</td>\n",
       "      <td>39.447543</td>\n",
       "      <td>40.615895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(5)</td>\n",
       "      <td>5.0</td>\n",
       "      <td>48.033824</td>\n",
       "      <td>0.341387</td>\n",
       "      <td>46.981772</td>\n",
       "      <td>49.085875</td>\n",
       "      <td>47.364717</td>\n",
       "      <td>48.702931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(6)</td>\n",
       "      <td>6.0</td>\n",
       "      <td>62.977430</td>\n",
       "      <td>0.319961</td>\n",
       "      <td>61.991407</td>\n",
       "      <td>63.963452</td>\n",
       "      <td>62.350318</td>\n",
       "      <td>63.604542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(7)</td>\n",
       "      <td>7.0</td>\n",
       "      <td>71.972310</td>\n",
       "      <td>0.341649</td>\n",
       "      <td>70.919451</td>\n",
       "      <td>73.025169</td>\n",
       "      <td>71.302690</td>\n",
       "      <td>72.641930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(8)</td>\n",
       "      <td>8.0</td>\n",
       "      <td>81.030147</td>\n",
       "      <td>0.409712</td>\n",
       "      <td>79.767541</td>\n",
       "      <td>82.292754</td>\n",
       "      <td>80.227127</td>\n",
       "      <td>81.833168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(9)</td>\n",
       "      <td>9.0</td>\n",
       "      <td>89.992871</td>\n",
       "      <td>0.428694</td>\n",
       "      <td>88.671766</td>\n",
       "      <td>91.313977</td>\n",
       "      <td>89.152646</td>\n",
       "      <td>90.833097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(10)</td>\n",
       "      <td>10.0</td>\n",
       "      <td>99.037858</td>\n",
       "      <td>0.481933</td>\n",
       "      <td>97.552688</td>\n",
       "      <td>100.523028</td>\n",
       "      <td>98.093287</td>\n",
       "      <td>99.982429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(11)</td>\n",
       "      <td>11.0</td>\n",
       "      <td>108.015015</td>\n",
       "      <td>0.566158</td>\n",
       "      <td>106.270287</td>\n",
       "      <td>109.759742</td>\n",
       "      <td>106.905364</td>\n",
       "      <td>109.124665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(12)</td>\n",
       "      <td>12.0</td>\n",
       "      <td>129.986895</td>\n",
       "      <td>0.047488</td>\n",
       "      <td>129.840552</td>\n",
       "      <td>130.133239</td>\n",
       "      <td>129.893820</td>\n",
       "      <td>130.079970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(13)</td>\n",
       "      <td>13.0</td>\n",
       "      <td>139.997579</td>\n",
       "      <td>0.048637</td>\n",
       "      <td>139.847693</td>\n",
       "      <td>140.147464</td>\n",
       "      <td>139.902251</td>\n",
       "      <td>140.092906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(14)</td>\n",
       "      <td>14.0</td>\n",
       "      <td>149.958472</td>\n",
       "      <td>0.051223</td>\n",
       "      <td>149.800619</td>\n",
       "      <td>150.116324</td>\n",
       "      <td>149.858077</td>\n",
       "      <td>150.058866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(15)</td>\n",
       "      <td>15.0</td>\n",
       "      <td>159.993152</td>\n",
       "      <td>0.049177</td>\n",
       "      <td>159.841602</td>\n",
       "      <td>160.144701</td>\n",
       "      <td>159.896766</td>\n",
       "      <td>160.089538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(16)</td>\n",
       "      <td>16.0</td>\n",
       "      <td>169.994772</td>\n",
       "      <td>0.054457</td>\n",
       "      <td>169.826954</td>\n",
       "      <td>170.162591</td>\n",
       "      <td>169.888040</td>\n",
       "      <td>170.101505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>dynamic</td>\n",
       "      <td>ATT(17)</td>\n",
       "      <td>17.0</td>\n",
       "      <td>180.077933</td>\n",
       "      <td>0.057241</td>\n",
       "      <td>179.901533</td>\n",
       "      <td>180.254334</td>\n",
       "      <td>179.965742</td>\n",
       "      <td>180.190124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       type      term  event.time    estimate  std.error    conf.low  \\\n",
       "1   dynamic  ATT(-17)       -17.0   -0.017531   0.027248   -0.101502   \n",
       "2   dynamic  ATT(-16)       -16.0   -0.009897   0.026398   -0.091246   \n",
       "3   dynamic  ATT(-15)       -15.0    0.033861   0.025256   -0.043969   \n",
       "4   dynamic  ATT(-14)       -14.0   -0.008687   0.026462   -0.090234   \n",
       "5   dynamic  ATT(-13)       -13.0    0.010769   0.025868   -0.068949   \n",
       "6   dynamic  ATT(-12)       -12.0   -0.024517   0.024753   -0.100798   \n",
       "7   dynamic  ATT(-11)       -11.0   -0.002178   0.020089   -0.064086   \n",
       "8   dynamic  ATT(-10)       -10.0    0.024891   0.018604   -0.032442   \n",
       "9   dynamic   ATT(-9)        -9.0   -0.016160   0.018425   -0.072940   \n",
       "10  dynamic   ATT(-8)        -8.0    0.017224   0.019280   -0.042192   \n",
       "11  dynamic   ATT(-7)        -7.0   -0.000583   0.017486   -0.054470   \n",
       "12  dynamic   ATT(-6)        -6.0   -0.026200   0.021407   -0.092169   \n",
       "13  dynamic   ATT(-5)        -5.0    0.014351   0.016567   -0.036703   \n",
       "14  dynamic   ATT(-4)        -4.0   -0.014417   0.016100   -0.064032   \n",
       "15  dynamic   ATT(-3)        -3.0   -0.009011   0.016761   -0.060662   \n",
       "16  dynamic   ATT(-2)        -2.0   -0.007238   0.016655   -0.058563   \n",
       "17  dynamic   ATT(-1)        -1.0    0.009600   0.016298   -0.040625   \n",
       "18  dynamic    ATT(0)         0.0    8.002456   0.062152    7.810924   \n",
       "19  dynamic    ATT(1)         1.0   16.010234   0.124710   15.625914   \n",
       "20  dynamic    ATT(2)         2.0   24.020465   0.167827   23.503274   \n",
       "21  dynamic    ATT(3)         3.0   32.013758   0.225303   31.319443   \n",
       "22  dynamic    ATT(4)         4.0   40.031719   0.298055   39.113206   \n",
       "23  dynamic    ATT(5)         5.0   48.033824   0.341387   46.981772   \n",
       "24  dynamic    ATT(6)         6.0   62.977430   0.319961   61.991407   \n",
       "25  dynamic    ATT(7)         7.0   71.972310   0.341649   70.919451   \n",
       "26  dynamic    ATT(8)         8.0   81.030147   0.409712   79.767541   \n",
       "27  dynamic    ATT(9)         9.0   89.992871   0.428694   88.671766   \n",
       "28  dynamic   ATT(10)        10.0   99.037858   0.481933   97.552688   \n",
       "29  dynamic   ATT(11)        11.0  108.015015   0.566158  106.270287   \n",
       "30  dynamic   ATT(12)        12.0  129.986895   0.047488  129.840552   \n",
       "31  dynamic   ATT(13)        13.0  139.997579   0.048637  139.847693   \n",
       "32  dynamic   ATT(14)        14.0  149.958472   0.051223  149.800619   \n",
       "33  dynamic   ATT(15)        15.0  159.993152   0.049177  159.841602   \n",
       "34  dynamic   ATT(16)        16.0  169.994772   0.054457  169.826954   \n",
       "35  dynamic   ATT(17)        17.0  180.077933   0.057241  179.901533   \n",
       "\n",
       "     conf.high  point.conf.low  point.conf.high  \n",
       "1     0.066439       -0.070937         0.035874  \n",
       "2     0.071452       -0.061635         0.041842  \n",
       "3     0.111691       -0.015639         0.083361  \n",
       "4     0.072860       -0.060551         0.043177  \n",
       "5     0.090486       -0.039932         0.061469  \n",
       "6     0.051764       -0.073032         0.023998  \n",
       "7     0.059730       -0.041552         0.037196  \n",
       "8     0.082223       -0.011573         0.061354  \n",
       "9     0.040619       -0.052272         0.019952  \n",
       "10    0.076639       -0.020565         0.055012  \n",
       "11    0.053303       -0.034856         0.033689  \n",
       "12    0.039770       -0.068156         0.015757  \n",
       "13    0.065405       -0.018120         0.046821  \n",
       "14    0.035199       -0.045973         0.017139  \n",
       "15    0.042640       -0.041861         0.023839  \n",
       "16    0.044086       -0.039881         0.025404  \n",
       "17    0.059824       -0.022343         0.041542  \n",
       "18    8.193988        7.880641         8.124271  \n",
       "19   16.394553       15.765806        16.254662  \n",
       "20   24.537655       23.691531        24.349399  \n",
       "21   32.708073       31.572172        32.455344  \n",
       "22   40.950232       39.447543        40.615895  \n",
       "23   49.085875       47.364717        48.702931  \n",
       "24   63.963452       62.350318        63.604542  \n",
       "25   73.025169       71.302690        72.641930  \n",
       "26   82.292754       80.227127        81.833168  \n",
       "27   91.313977       89.152646        90.833097  \n",
       "28  100.523028       98.093287        99.982429  \n",
       "29  109.759742      106.905364       109.124665  \n",
       "30  130.133239      129.893820       130.079970  \n",
       "31  140.147464      139.902251       140.092906  \n",
       "32  150.116324      149.858077       150.058866  \n",
       "33  160.144701      159.896766       160.089538  \n",
       "34  170.162591      169.888040       170.101505  \n",
       "35  180.254334      179.965742       180.190124  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estimate model, using the data converted to an R/rpy2 DataFrame\n",
    "\n",
    "get_att_gt = did.att_gt(data = rbaker,\n",
    "          yname = 'y',\n",
    "          tname = 'time',\n",
    "          idname = 'individual',\n",
    "          gname = 'treat_date', \n",
    "          control_group = 'notyettreated')\n",
    "\n",
    "# Aggregate results and extract for converting to a Pandas df\n",
    "# You can toggle the aggregation type for whatever you want\n",
    "csdid_results_r = did.tidy_AGGTEobj(did.aggte(get_att_gt, type = \"dynamic\"))\n",
    "\n",
    "# The results table is an R/rpy2 dataframe. Convert this to a Pandas df\n",
    "with localconverter(ro.default_converter + pandas2ri.converter):\n",
    "    csdid_results = ro.conversion.rpy2py(csdid_results_r)\n",
    "\n",
    "# Print table\n",
    "csdid_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "584f1fa2",
   "metadata": {},
   "source": [
    "### Sun and Abraham (2020 )- uses sunab in the fixest R package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "89d30737",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate</th>\n",
       "      <th>std.error</th>\n",
       "      <th>statistic</th>\n",
       "      <th>p.value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>time::-18</td>\n",
       "      <td>0.035915</td>\n",
       "      <td>0.032784</td>\n",
       "      <td>1.095515</td>\n",
       "      <td>0.273555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>time::-17</td>\n",
       "      <td>0.018963</td>\n",
       "      <td>0.031815</td>\n",
       "      <td>0.596020</td>\n",
       "      <td>0.551297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>time::-16</td>\n",
       "      <td>0.014402</td>\n",
       "      <td>0.031991</td>\n",
       "      <td>0.450207</td>\n",
       "      <td>0.652659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>time::-15</td>\n",
       "      <td>0.046738</td>\n",
       "      <td>0.031558</td>\n",
       "      <td>1.481032</td>\n",
       "      <td>0.138913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>time::-14</td>\n",
       "      <td>0.037620</td>\n",
       "      <td>0.032466</td>\n",
       "      <td>1.158753</td>\n",
       "      <td>0.246834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>time::-13</td>\n",
       "      <td>0.043390</td>\n",
       "      <td>0.031413</td>\n",
       "      <td>1.381278</td>\n",
       "      <td>0.167502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>time::-12</td>\n",
       "      <td>-0.010092</td>\n",
       "      <td>0.022505</td>\n",
       "      <td>-0.448418</td>\n",
       "      <td>0.653949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>time::-11</td>\n",
       "      <td>-0.019476</td>\n",
       "      <td>0.023937</td>\n",
       "      <td>-0.813638</td>\n",
       "      <td>0.416046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>time::-10</td>\n",
       "      <td>0.010229</td>\n",
       "      <td>0.022202</td>\n",
       "      <td>0.460713</td>\n",
       "      <td>0.645105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>time::-9</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>0.022995</td>\n",
       "      <td>0.112761</td>\n",
       "      <td>0.910243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>time::-8</td>\n",
       "      <td>0.003286</td>\n",
       "      <td>0.022682</td>\n",
       "      <td>0.144861</td>\n",
       "      <td>0.884850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>time::-7</td>\n",
       "      <td>0.021919</td>\n",
       "      <td>0.019115</td>\n",
       "      <td>1.146721</td>\n",
       "      <td>0.251772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>time::-6</td>\n",
       "      <td>-0.004519</td>\n",
       "      <td>0.018791</td>\n",
       "      <td>-0.240504</td>\n",
       "      <td>0.809989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>time::-5</td>\n",
       "      <td>0.004720</td>\n",
       "      <td>0.018478</td>\n",
       "      <td>0.255434</td>\n",
       "      <td>0.798440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>time::-4</td>\n",
       "      <td>-0.001460</td>\n",
       "      <td>0.017715</td>\n",
       "      <td>-0.082398</td>\n",
       "      <td>0.934347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>time::-3</td>\n",
       "      <td>-0.008617</td>\n",
       "      <td>0.018591</td>\n",
       "      <td>-0.463497</td>\n",
       "      <td>0.643109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>time::-2</td>\n",
       "      <td>-0.017940</td>\n",
       "      <td>0.018094</td>\n",
       "      <td>-0.991467</td>\n",
       "      <td>0.321697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>time::0</td>\n",
       "      <td>7.988848</td>\n",
       "      <td>0.018820</td>\n",
       "      <td>424.491204</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>time::1</td>\n",
       "      <td>15.999942</td>\n",
       "      <td>0.018842</td>\n",
       "      <td>849.150018</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>time::2</td>\n",
       "      <td>24.010756</td>\n",
       "      <td>0.018823</td>\n",
       "      <td>1275.599257</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>time::3</td>\n",
       "      <td>31.998402</td>\n",
       "      <td>0.019294</td>\n",
       "      <td>1658.469666</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>time::4</td>\n",
       "      <td>40.015793</td>\n",
       "      <td>0.019613</td>\n",
       "      <td>2040.247009</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>time::5</td>\n",
       "      <td>48.028399</td>\n",
       "      <td>0.017690</td>\n",
       "      <td>2714.940441</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>time::6</td>\n",
       "      <td>62.970106</td>\n",
       "      <td>0.026900</td>\n",
       "      <td>2340.905592</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>time::7</td>\n",
       "      <td>71.976302</td>\n",
       "      <td>0.026361</td>\n",
       "      <td>2730.442578</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>time::8</td>\n",
       "      <td>81.024332</td>\n",
       "      <td>0.028313</td>\n",
       "      <td>2861.744284</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>time::9</td>\n",
       "      <td>89.977726</td>\n",
       "      <td>0.028145</td>\n",
       "      <td>3196.981855</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>time::10</td>\n",
       "      <td>99.032793</td>\n",
       "      <td>0.029504</td>\n",
       "      <td>3356.586229</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>time::11</td>\n",
       "      <td>108.004167</td>\n",
       "      <td>0.031481</td>\n",
       "      <td>3430.823218</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>time::12</td>\n",
       "      <td>129.986895</td>\n",
       "      <td>0.046425</td>\n",
       "      <td>2799.935021</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>time::13</td>\n",
       "      <td>139.997579</td>\n",
       "      <td>0.046990</td>\n",
       "      <td>2979.289815</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>time::14</td>\n",
       "      <td>149.958472</td>\n",
       "      <td>0.049833</td>\n",
       "      <td>3009.230467</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>time::15</td>\n",
       "      <td>159.993152</td>\n",
       "      <td>0.049708</td>\n",
       "      <td>3218.688808</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>time::16</td>\n",
       "      <td>169.994772</td>\n",
       "      <td>0.054744</td>\n",
       "      <td>3105.282559</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>time::17</td>\n",
       "      <td>180.077933</td>\n",
       "      <td>0.056376</td>\n",
       "      <td>3194.210422</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         term    estimate  std.error    statistic   p.value\n",
       "1   time::-18    0.035915   0.032784     1.095515  0.273555\n",
       "2   time::-17    0.018963   0.031815     0.596020  0.551297\n",
       "3   time::-16    0.014402   0.031991     0.450207  0.652659\n",
       "4   time::-15    0.046738   0.031558     1.481032  0.138913\n",
       "5   time::-14    0.037620   0.032466     1.158753  0.246834\n",
       "6   time::-13    0.043390   0.031413     1.381278  0.167502\n",
       "7   time::-12   -0.010092   0.022505    -0.448418  0.653949\n",
       "8   time::-11   -0.019476   0.023937    -0.813638  0.416046\n",
       "9   time::-10    0.010229   0.022202     0.460713  0.645105\n",
       "10   time::-9    0.002593   0.022995     0.112761  0.910243\n",
       "11   time::-8    0.003286   0.022682     0.144861  0.884850\n",
       "12   time::-7    0.021919   0.019115     1.146721  0.251772\n",
       "13   time::-6   -0.004519   0.018791    -0.240504  0.809989\n",
       "14   time::-5    0.004720   0.018478     0.255434  0.798440\n",
       "15   time::-4   -0.001460   0.017715    -0.082398  0.934347\n",
       "16   time::-3   -0.008617   0.018591    -0.463497  0.643109\n",
       "17   time::-2   -0.017940   0.018094    -0.991467  0.321697\n",
       "18    time::0    7.988848   0.018820   424.491204  0.000000\n",
       "19    time::1   15.999942   0.018842   849.150018  0.000000\n",
       "20    time::2   24.010756   0.018823  1275.599257  0.000000\n",
       "21    time::3   31.998402   0.019294  1658.469666  0.000000\n",
       "22    time::4   40.015793   0.019613  2040.247009  0.000000\n",
       "23    time::5   48.028399   0.017690  2714.940441  0.000000\n",
       "24    time::6   62.970106   0.026900  2340.905592  0.000000\n",
       "25    time::7   71.976302   0.026361  2730.442578  0.000000\n",
       "26    time::8   81.024332   0.028313  2861.744284  0.000000\n",
       "27    time::9   89.977726   0.028145  3196.981855  0.000000\n",
       "28   time::10   99.032793   0.029504  3356.586229  0.000000\n",
       "29   time::11  108.004167   0.031481  3430.823218  0.000000\n",
       "30   time::12  129.986895   0.046425  2799.935021  0.000000\n",
       "31   time::13  139.997579   0.046990  2979.289815  0.000000\n",
       "32   time::14  149.958472   0.049833  3009.230467  0.000000\n",
       "33   time::15  159.993152   0.049708  3218.688808  0.000000\n",
       "34   time::16  169.994772   0.054744  3105.282559  0.000000\n",
       "35   time::17  180.077933   0.056376  3194.210422  0.000000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Estimate model, using your dataset converted to an R/rpy2 DataFrame\n",
    "\n",
    "sunab_mod = fixest.feols(fml = Formula(\"y ~ sunab(treat_date, time) | individual + time\"), \n",
    "             subset = Formula('~year < 2004'),\n",
    "             data = rbaker)\n",
    "\n",
    "# Get results table \n",
    "sunab_resultsr = broom.tidy_fixest(sunab_mod)\n",
    "\n",
    "# The results table is an R/rpy2 dataframe. Convert this to a Pandas df\n",
    "with localconverter(ro.default_converter + pandas2ri.converter):\n",
    "    sunab_results = ro.conversion.rpy2py(sunab_resultsr)\n",
    "\n",
    "# Print table\n",
    "sunab_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "a4346683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'DID Estimators - Baker data')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x1080 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a plot for the estimators that are suitable for our data/model\n",
    "\n",
    "fig = plt.figure(figsize = (25, 15))\n",
    "\n",
    "ax1 = fig.add_subplot(2, 2, 1)\n",
    "het_coef_df.plot(x = 'varname', y = 'coef', kind = 'bar', \n",
    "             ax = ax1, color = 'none', \n",
    "             yerr = 'err', legend = False)\n",
    "ax1.set_ylabel('')\n",
    "ax1.set_xlabel('')\n",
    "ax1.axhline(y = 0, color= 'r', linestyle='-')\n",
    "ax1.scatter(x = np.arange(het_coef_df.shape[0]), \n",
    "           marker = 'o', s = 10, \n",
    "           y = het_coef_df['coef'], color = 'black')\n",
    "ax1.set_title('TWFE', size = 20)\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(2, 2, 2)\n",
    "sunab_results.plot(x = 'term', y = 'estimate', kind = 'bar', \n",
    "             ax = ax2, color = 'none', \n",
    "             yerr ='std.error', legend = False)\n",
    "ax2.axhline(y = 0, color='r', linestyle='-')\n",
    "ax2.scatter(x = np.arange(sunab_results.shape[0]), \n",
    "           marker = 'o', s = 10, \n",
    "           y = sunab_results['estimate'], color = 'black')\n",
    "ax2.set_title('Sun and Abraham (2020)', size = 20)\n",
    "\n",
    "ax3 = fig.add_subplot(2, 2, 3)\n",
    "csdid_results.plot(x = 'term', y = 'estimate', kind = 'bar', \n",
    "             ax = ax3, color = 'none', \n",
    "             yerr ='std.error', legend = False)\n",
    "ax3.axhline(y = 0, color='r', linestyle='-')\n",
    "ax3.scatter(x = np.arange(csdid_results.shape[0]), \n",
    "           marker = 'o', s = 10, \n",
    "           y = csdid_results['estimate'], color = 'black')\n",
    "ax3.set_title(\"Callaway and Sant'Anna (2020)\", size = 20)\n",
    "\n",
    "\n",
    "fig.suptitle(\"DID Estimators - Baker data\", fontweight = 'bold', size = 20)"
   ]
  }
 ],
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